An advanced postgraduate course covering the theory and practice of deep learning for data mining tasks. Topics include neural network architectures, convolutional and recurrent networks, attention mechanisms, and the application of deep learning to structured and unstructured data.

This postgraduate course examines the design and implementation of intelligent software agents and multi-agent systems. Topics include agent architectures, communication protocols, negotiation, coordination, and the application of multi-agent systems in complex and distributed environments.

A final-year undergraduate course covering core principles and practices of computer and information security. Topics include cryptography, network security, access control, vulnerability assessment, ethical hacking, and the legal and ethical dimensions of cybersecurity.

A third-year course introducing core AI concepts and techniques. Topics include search and planning, knowledge representation, machine learning fundamentals, natural language processing, and an introduction to the ethical and societal implications of AI systems.

This course provides a comprehensive understanding of modern operating system design and implementation. Topics include process and memory management, scheduling, concurrency, file systems, and the principles underlying secure and efficient OS architecture.

An exploration of the principles, architectures, and security challenges of distributed computing systems. Topics include distributed algorithms, consistency and consensus, fault tolerance, remote procedure calls, middleware, and security in distributed environments.

An introductory course teaching the fundamentals of object-oriented programming. Students develop skills in abstraction, encapsulation, inheritance, and polymorphism, applying these concepts through practical programming exercises and small-scale software projects.

A foundational course introducing students to the nature, representation, and analysis of data. Topics include data types, basic statistics, data visualisation, and an introduction to the tools and thinking needed to reason critically with data in real-world contexts.

An interdisciplinary Level 1 course exploring how computing and technology interact with the physical world. Students are introduced to sensors, signals, physical computing, and the broader context of technology in society and the natural environment.

Guided Master's students in the Human-Centred AI programme through the complexities of developing AI systems that are transparent, fair, and trustworthy. Topics included interpretability, bias mitigation, and responsible AI development.

Introduced undergraduate Data Science students to foundational and cutting-edge deep learning techniques, including neural networks, CNNs, RNNs, and generative adversarial networks (GANs).

Equipped Master's students with skills for rigorous research across quantitative, qualitative, and mixed methods approaches. Covered research design, data collection, analysis, and ethical considerations.

A project-based course where student teams applied engineering knowledge to real-world challenges, engaging in all phases from problem identification and needs analysis through to prototyping, testing, and evaluation.

A comprehensive overview of tools and technologies used by data scientists, including Python, R, SQL, cloud platforms (AWS, Google Cloud), and big data technologies such as Hadoop and Spark.

Introduced students to the principles and practices of Human-Centred Design, covering user research, empathy mapping, prototyping, and user testing as part of an iterative, user-focused design process.

An introductory undergraduate course covering the foundational concepts of data — its nature, representation, and analysis. Topics included data types, basic statistics, data wrangling, and visualisation, providing students with the core skills to reason critically and work effectively with real-world datasets.

An undergraduate course introducing the theory and practice of machine learning. Topics included supervised and unsupervised learning, model evaluation, feature engineering, and the practical application of ML algorithms using modern programming tools, with emphasis on real-world problem solving.